Erndob

Besides how well something works, I am really curious if there’s any divergence that comes from different grammar in languages.

As in, the way languages are structured is different. Some are more precise, some are less, the information density per syllable is different, etc.

So besides just pure performance due to differences in training data, I’m curious if there’s some fundamental difference in the way LLMs interact with data in different languages even if end information is the same. Because even just in English, phrasing slightly different can yield different results.

Edit: would be interesting to see the “thinking” of the model done in different languages. Is identical problem thought about more or less the same, or does agent go on different train of thought depending on the language it is thinking in?

Which also conveniently makes you spend more money on tokens.

With agile, at least no one was charging you for it. Like sure, there’s a cost to the process. But there wasn’t direct agile.com profiting from you.

Meanwhile agentic workflows every solution to the problem is giving more money to the ai companies.

Model is bad? Made more expensive model. Still bad? Here’s an infrastructure that reads huge text files again and again making you consume tokens. Still bad? Here’s a way to easily spin up multiple agents at once so you can delegate work. Still bad? Here’s a new service that will automatically review code. Still bad? Maybe a biggger more expensive model will help.

Microsoft managed to introduce a critical vulnerability in Notepad, so this does not surprise me